Challenges, Limitations, and Ethical Considerations of AI in Immunology and Healthcare

Abstract

Artificial Intelligence (AI) is transforming immunology and healthcare by enabling advanced diagnostics, personalized treatments, and data-driven decision-making. However, its implementation is fraught with challenges, including the complexity of immune networks, variability in patient responses, and data quality issues. AI models often struggle to understand the variations that occur among diverse human populations and adapt to the dynamic concepts of immunology. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, further complicate its integration into clinical practice. Logistical barriers, including resource constraints and regulatory hurdles, limit accessibility, especially in low-resource settings, although it has the potential to diminish inequalities in access to care and medical information. This chapter explores these challenges while emphasizing solutions, such as interdisciplinary collaboration, adaptive frameworks, and equitable data sharing, to maximize AI's potential in advancing immunological research and global healthcare outcomes.

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